원클릭으로
ai-friendly-audit
Lightweight AI Friendliness check for a repository (no subagents, within 5 minutes). Quickly prioritizes basic improvements.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
메뉴
Lightweight AI Friendliness check for a repository (no subagents, within 5 minutes). Quickly prioritizes basic improvements.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Orchestrate a full development pipeline (Analysis → Investigation → Design → AI Review → Tasks → AI Review → Implementation → Review) using isolated subagents to prevent context contamination and conserve tokens.
Evaluate a request and recommend the appropriate workflow — GitHub issue only, GitHub issue + SDD spec, or full SDD spec — based on scope, complexity, and architectural impact.
| name | ai-friendly-audit |
| description | Lightweight AI Friendliness check for a repository (no subagents, within 5 minutes). Quickly prioritizes basic improvements. |
Quickly check a repository's AI agent readiness in a single pass without subagents, and identify the most important basics to address first.
The full version (ai-friendly-audit-full) is precise with 21 Probes × 5 Workers but has high token cost. The Lite version uses 7 checks to identify "what to do first", establishing a workflow where basics are addressed before deep-diving with the full version.
Lite basic check → Fix basics → Full precise evaluation → Continuous improvement
Run the following 7 checks in order. Each check completes within a few tool calls.
Purpose: Understand scale and tech stack. Establishes context for subsequent checks.
Steps:
# File count by language
find . -type f \( -name "*.go" -o -name "*.ts" -o -name "*.tsx" -o -name "*.js" -o -name "*.jsx" -o -name "*.py" -o -name "*.rb" -o -name "*.rs" -o -name "*.java" -o -name "*.kt" -o -name "*.cs" -o -name "*.swift" -o -name "*.php" \) \
| grep -v node_modules | grep -v vendor | grep -v '.git/' | grep -v '/gen/' | grep -v '/generated/' \
| sed 's/.*\.//' | sort | uniq -c | sort -rn
# Build system
ls -1 go.mod package.json pnpm-lock.yaml yarn.lock Cargo.toml pyproject.toml Gemfile build.gradle pom.xml Makefile 2>/dev/null
# Monorepo
ls -1 go.work pnpm-workspace.yaml lerna.json nx.json turbo.json 2>/dev/null
Record: Primary language, file count, scale (small/medium/large)
Purpose: Verify existence of configuration files auto-loaded by each AI Agent. The first step in AI readiness.
Steps: Confirm existence of the following files at once:
Glob("AGENTS.md")
Glob("CLAUDE.md")
Glob(".claude/rules/*.md")
Glob(".claude/skills/*/SKILL.md")
Glob(".cursor/rules/*.md")
Glob(".cursor/skills/*/SKILL.md")
Glob(".github/copilot-instructions.md")
Glob(".windsurfrules")
Scoring:
| Score | Criteria |
|---|---|
| A | Rule files for 3+ Agents exist |
| B | Rule files for 1-2 Agents exist |
| C | No rule files |
Record: List of existing files and missing Agents
Purpose: Verify that the document an AI should read first contains "usable" content.
Steps:
git log -1 --format="%ai" -- README.mdScoring:
| Score | Criteria |
|---|---|
| A | 4/4 items present + updated within 6 months |
| B | 2-3/4 items present, or outdated (over 6 months) |
| C | 1/4 or fewer, or no README |
Purpose: Can code locations be inferred from feature names?
Steps:
Glob("**/{feature-name}*/**") once for each feature nameScoring:
| Score | Criteria |
|---|---|
| A | 2/2 found on first try |
| B | 1/2 successful |
| C | 0/2 successful |
Purpose: How well is implicit knowledge documented?
Steps: Check whether the following 3 categories are described in Agent auto-loaded documents (files found in Check 2):
make verify, pnpm test explicitly stated?DO NOT, NEVER, MUST NOT, etc.?Search each category in Agent rule files + README/ARCHITECTURE.md:
Grep("(make |pnpm |npm |cargo |pytest|go test)", glob="{CLAUDE.md,AGENTS.md,README.md,.claude/rules/*.md,.cursor/rules/*.md}", output_mode="content", head_limit=10)
Grep("(DO NOT|NEVER|MUST NOT|IMPORTANT)", glob="{CLAUDE.md,AGENTS.md,README.md,.claude/rules/*.md,.cursor/rules/*.md}", output_mode="content", head_limit=10)
Grep("(generate|gen|codegen)", glob="{CLAUDE.md,AGENTS.md,README.md,.claude/rules/*.md,.cursor/rules/*.md,ARCHITECTURE.md}", output_mode="content", head_limit=10)
Scoring:
| Score | Criteria |
|---|---|
| A | 3/3 categories explicitly in Agent rule files |
| B | 1-2/3 documented, or in README but not Agent rules |
| C | Barely documented |
Purpose: Verify code pattern uniformity at minimum cost.
Steps:
ls each)Scoring:
| Score | Criteria |
|---|---|
| A | Match rate 75%+ |
| B | Match rate 50-74% |
| C | Match rate below 50% |
Purpose: Is architecture/coding convention documentation close to the code?
Steps:
Glob("**/ARCHITECTURE.md")
Glob("**/CODING_RULES.md")
Glob("**/CONTRIBUTING.md")
Glob("**/DEVELOPMENT.md")
Glob("**/STYLE_GUIDE.md")
Read the first 10 lines of found files and confirm they have substantive content (templates alone do not qualify).
Scoring:
| Score | Criteria |
|---|---|
| A | 2+ files with substantive content |
| B | 1 file only |
| C | None, or templates only |
A=3, B=2, C=1 per check, total (out of 21):
| Grade | Total | Meaning | Next Action |
|---|---|---|---|
| S | 19-21 | Basics are sufficient | → Proceed to full version (/ai-friendly-audit-full) for precise evaluation |
| A | 15-18 | Generally good. Some reinforcement needed | → Improve C-rated items, then proceed to full version |
| B | 11-14 | Basics are lacking | → Implement improvement suggestions below |
| C | 7-10 | AI assistance is difficult | → Prioritize improvement suggestions below |
Output directly to terminal, then save the report to a file. Be concise.
After displaying results in the terminal, save the same report to:
docs/ai-friendly-audit-report/{YYYY-MM-DD_HH-MM-SS}.md
date +"%Y-%m-%d_%H-%M-%S".docs/ai-friendly-audit-report/ is excluded from the bilingual requirement (see .claude/rules/docs.md).## AI-Friendly Audit Lite
**Repository**: {name}
**Scale**: {small/medium/large} ({n} source files)
**Tech Stack**: {languages}
### Check Results
| # | Check Item | Score | Summary |
|---|-----------|-------|---------|
| 1 | Repository Profile | — | {scale and language summary} |
| 2 | AI Agent Config Files | {A/B/C} | {present/absent summary} |
| 3 | Entry Point Document | {A/B/C} | {quality summary} |
| 4 | Directory Predictability | {A/B/C} | {result summary} |
| 5 | Convention Documentation | {A/B/C} | {coverage summary} |
| 6 | Pattern Consistency | {A/B/C} | {match rate} |
| 7 | Architecture Documents | {A/B/C} | {detection summary} |
### Overall Grade: {S/A/B/C} ({n}/21)
### Improvement Actions (by priority)
For C-rated items, provide specific improvement actions in 1-3 lines.
1. **{Check name}**: {what to do}
2. ...
### Next Step
- Grade S → `Run the full audit to identify precise improvement areas`
- Grade A/B/C → `Implement the improvements above, then re-run Lite. Once you reach S, proceed to the full version`